World Bank Group
L o a d i n g
The World Bank Group has set two goals for the world to achieve by 2030: (i) End extreme poverty by decreasing the percentage of people living on less than $1.90 a day to no more than 3%, and (ii) promote shared prosperity by fostering the income growth of the bottom 40% for every country. The World Bank is a vital source of financial and technical assistance to developing countries around the world.
Available DatasetsShowing 10 of 10 results
- This dataset is a global inventory of known solar stations for which there is access to corresponding solar radiation measurement data, which aims to help improve developing country's knowledge and awareness of solar resources. Users are encouraged to contribute to the development of this dataset. If you have access to solar radiation data, please contact us for uploading the data on the platform and referencing it in the Global Solar Atlas: http://globalsolaratlas.info/1Licence not specifiedabout 7 hours ago
- This raster file represents a land cover classification in Santa Ana based off satellite imagery from February 22, 2015. This land cover classification was used in the following report: Mapping Land Cover in Secondary Cities in Central America. This work was initiated as an analytical effort to fill a gap on spatial form of secondary cities. While this is an independent output, this work is tightly linked to the work done under the Central America Urbanization Review. The analysis here described, was used as an input in the definition of urban agglomerations used in the Urbanization Review. The detailed analysis on secondary cities is seen as a complement to the work carried out in the Urbanization Review, in that it zooms into what is happening within a set of cities. The Urbanization Review instead provides a broader look at the system of cities in Central America, highlighting the key bottlenecks the regions faces in moving toward more inclusive, productive, and resilient cities. The Urbanization Review can be found here: http://documents.worldbank.org/curated/en/134151467994680764/pdf/106268-...1Licence not specified10 days ago
- This paper studies the impact of transport infrastructure projects of the Belt and Road Initiative on shipment times and trade costs. Based on a new data on completed and planned Belt and Road transport projects, Geographic Information System analysis is used to estimate shipment times before and after the Belt and Road Initiative. Two sets of data are computed to address different research questions: a global database based on an analysis of 1,000 cities in 191 countries and 47 sectors and a regional database that focuses on more granular information (1,818 cities) for Belt and Road economies only.1Licence not specified10 days ago
- Tracking SDG7: The Energy Progress Report provides the international community with a global dashboard to register progress on the targets of Sustainable Development Goal 7 (SDG7): ensuring universal energy access, doubling progress on energy efficiency, substantially increasing the share of renewable energy, and enhance international cooperation to facilitate access to clean and renewable energy by 2030. It assesses the progress made by each country on these targets and provides a snapshot of how far we are from achieving SDG7. The 2020 release is the sixth edition of this report, which was formerly known as the Global Tracking Framework (GTF).1Licence not specified10 days ago
- This paper presents a database of indicators of innovative activity around the world since the early 1960s. The data include measures of innovation outcomes as well as variables related to innovation effort. The main indicator of innovation outputs is patents. The main variables related to innovation inputs are investment in research and development (R&D) and technical personnel (engineers, scientists) working in R&D activities. The sources of these data are publicly available (OECD, UNESCO, etc.), yet there have been few attempts at double checking the consistency of these data and digitizing observations dating back to the 1960s.1Licence not specified10 days ago
- Health facilities in Kenya were acquired from a local consultant through the KEMRI Wellcome Trust Research Programme. These facilities were used to assess hospital preparedness based on available infrastructure data (GSM coverage, electrification, access (drive time)) and demographics (population and vulnerability to CoVID). Additionally summary statistics were generated at the ward level.1Licence not specified10 days ago
- The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis. Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes. This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).1Licence not specified10 days ago
- The goal of the South West Indian Ocean Risk Assessment and Financing Initiative (SWIO RAFI) is to improve the resiliency and capacity of the island states through the creation of disaster risk financing strategies. A key component of this effort involves the quantification of site specific risk from the perils of flood, earthquakes, and tropical cyclones as well as their secondary hazards of storm surge and tsunamis. Regional hazard intensity calculations were applied to 10,000 years of Stochastic catalogs derived from the historical records to produce hazard intensity profiles at mean return periods of 25, 50, 100, 250, 500 and 1,000 years. All datasets are at their original resolution (0.00083) except for Madagascar (0.0032) which was resampled to reduce file sizes. This data set was produced with financial support from the European Union in the framework of the ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR).1Licence not specified10 days ago
- These raster files represent land cover classifications in Trinidad, Bolivia at two different time periods: March 7, 2007 and June 15, 2015. In order to better understand the changing landscape of Trinidad, imagery covering the entire city was acquired at two different time periods (2007 and 2015). These high resolution (50cm) scenes were then transformed into land cover maps using a methodology developed by Graesser et al (2012). Originally created to accurately detect shanties in major cities throughout the world, this method has been proven effective in a diverse set of cities (Kandahar, Kabul, Caracas, and La Paz). Furthermore, it has been shown to be effective at capturing land cover change in 5 primary cities in Africa by Antos et al 2016. Since its creation, it has been adopted by the US Census Bureau, US Department of Energy’s Oakridge Laboratory and The George Washington University. This semi-automated classification approach, examines the texture and structural composition of various neighborhoods, and then groups land with similar patterns into a single class. For the city of Trinidad, the images were divided into 9 distinct classes: Regular residential, Sparse residential, Flooded residential (only detected in 2005), commercial/industrial, roads, bare soil/dry grass, sand, vegetation, and water. Raster is coded by number, defined below: 1. Sparse Residential 2. Regular Residential, 3. Commercial/Industrial 4. Bare Soil/Dry Grass 5. Vegetation 6. Water 7. Sand 8. Road 9. Flooded residential (only detected in 2007)1Licence not specified10 days ago
- Crop residue information aggregated from the Biomass Atlas project field survey raw data. * Min minimum crop residue yield; based on minimum crop yield recorded in the field survey, and the minimum Residue To Crop Ratio in the background data (see: industrial\Power_plant_model.xlsx in this dataset, sheet RCR) Max maximum crop residue yield; based on maximum crop yield recorded in the field survey, and the maximum Residue To Crop Ratio in the background data (see: industrial\Power_plant_model.xlsx in this dataset, sheet RCR) Mean mean crop residue yield (theoretical potential); based on mean crop yield recorded in the field survey, and the mean Residue To Crop Ratio in the background data (see: industrial\Power_plant_model.xlsx in this dataset, sheet RCR) Std standard deviation of the mean crop residue yield in the survey data N number of observations for the crop in the survey data AR availability of the crop residue based on the current residue use recorded in the field survey (technical potential) ARS standard deviation of the availability of the crop residue based on the current residue use AW availability of the crop residue based on the current residue use recorded in the field survey and farmers' willingness to participate in a feedstock supply chain (technical potential) AWS standard deviation of the availability of the crop residue based on the current residue use recorded in the field survey and farmers' willingness to participate in a feedstock supply chain where denotes an abbreviation for a crop residue, as listed below. WheStrMin Wheat straw CotStaMin Cotton stalk RicStrMin Rice straw RicHusMin Rice husk MaiStaMin Maize stalk MaiCobMin Maize cob MaiHusMin Maize husk SugTraMin Sugarcane trash SugBagMin Bagasse Feedstock summary by country and by district, including sampled district confidence intervals for yearly feedstock amounts, t/yr You can find more information about the project here: https://www.esmap.org/node/3058 Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass Feedstock Crop Yield, 2016, https://energydata.info/dataset/pakistan-biomass-feedstock-cropyield"1Licence not specified10 days ago
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